<p>Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials. </p><i><p>Introduction to Statistical Machine Learning </i>provides a<i></i>general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at adva
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This is the first volume of the two-volume book on linear algebra, in the University of Tokyo (UTokyo) Engineering Course.The objective of this volume is to present, from the engineering viewpoint, the standard mathematical results in linear algebra such...
kr 719.00
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<p><b>Machine Learning: A Bayesian and Optimization Perspective, 2<SUP>nd</SUP> edition</b>, gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification....
kr 961.00
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The theory of data assimilation and machine learning is introduced in an accessible and pedagogical manner, with a focus on the underlying statistical physics. This modern and cross-disciplinary book is suitable for undergraduate and graduate students...
kr 719.00
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